Overview

As a leading credit rating agency from the United States, our client faced challenges acquiring reliable environmental, social, and governance (ESG) data at scale across web sources and multiple frameworks, which are crucial for credit assessments, risk management, and regulatory compliance. Unstructured data formats hindered accurate insights, affecting faster decision-making and market competitiveness. This highlighted the need for a streamlined workflow to improve efficiency and meet market demands.

Challenges

The client faced challenges in ESG data processing due to the massive volume and complexity of the frameworks. Using manual processes and traditional systems leads to a lack of real-time data, and fragmented reporting methodologies undermine compliance with regulatory standards without any unified view of ESG performance. The absence of intuitive analysis tools delayed investment decisions, complicating ESG reporting and reliability.

• Manually extracting ESG data was both time-consuming and error-prone.
• Integrating ESG data into existing systems was highly challenging.
• With the growing intricacy of ESG data, scalability has become questionable.
• Customizing ESG data visualization was a significant challenge.

The XDAS Approach

Catering to client’s ESG data needs, the XDAS team designed a comprehensive and structured workflow tailored to streamline ESG data processing, ensure accuracy, and ensure optimal regulatory compliance.

Seamless data extraction

To address the challenges, we first implemented the extractor bot, which captured data from text and image-based PDFs derived from diverse sources like BRSR, sustainability reports, CSR, IAR, CRISIL, and annual returns. We deployed a PDF Parser for image-based PDFs that converted them into text, enabling precise listing of over 1,500 ESG attributes.

Streamlined data processing

The workflow incorporated a GPT-enabled data stream to refine the extracted data, leveraging prompts to draw critical insights from the captured attributes. This process ensured that key ESG metrics were identified and organized for downstream processing. Lengthy reports were segmented into manageable chunks using a PDF Text Chunker for systematic and efficient data extraction.

Real-time regulatory tracking

The ESG Tracker kept the client informed by monitoring industry regulations and providing real-time updates on compliance requirements. This proactive feature ensured the client was aligned with fast-changing ESG standards and avoided regulatory pitfalls.

Multi-dimensional benchmarking

The XDAS platform enabled comprehensive performance analysis, offering insights into industry trends, peer comparisons, and portfolio performance. This way, clients can evaluate companies’ ESG practices at a granular level and compare and align performance with industry standards. This streamlined comparative analysis highlighted opportunities for improvement.

Human-in-the-Loop validation

The Human-in-the-Loop (HITL) mechanism was deployed through Mojo, where skilled human agents actively reviewed and validated the accuracy of the extracted data. These agents were crucial in resolving any missing or incorrect data within the auto-populated attributes in the automated data extraction process. This approach ensured that extracted data underwent rigorous quality control, mitigating risks due to errors and bolstering the reliability of the ESG reporting process.

Customizable reporting and dashboards

Integrating a report generator bot, the XDAS solution generated intuitive, customizable dashboards and downloadable reports in desirable data formats. This flexibility allowed stakeholders to acquire tailored outputs for their specific business requirements.

Results

90% Faster data

XDAS automated ESG data collection, speeding up compliance and decision-making.

99% Accuracy

GenAI and HITL workflows ensured 99% error-free ESG assessments.

Personalized insights at scale

Dynamic dashboards and customized reports provided actionable, real-time insights.

Seamless ESG integration

ESG data is integrated effortlessly into existing systems, optimizing operations.